Indeed, businesses are awash in data, and workforce data in particular as organizations have moved to digitize the entire employee lifecycle — from sourcing to offboarding.

Workforce data isn’t limited to the processes HR manages, though, and whether we are trying to connect learning programs to productivity, or are hiring for customer satisfaction, we must expand our view of what workforce data is.

Our broadest views of it are continually being expanded by the IoT (Internet of Things) and newer cloud applications that capture data automatically. With more data to learn from and greater analytical processing power, Artificial Intelligence has become an increasingly reliable source of talent predictions.

How will these developments impact the way that organizations (many of which are facing critical skills gap in a volatile business environment) make decisions about what is a company’s most important asset – it’s people?

Here are five talent strategy trends, fueled by developments of the data age, to watch in 2018:

#1. Building Better Teams With IoT Data

One useful source of IoT data for employers in 2018 will be sociometric badges, wearable devices equipped with sensors that measure team interactions. As explained by MIT scholar and serial entrepreneur Alex (Sandy) Pentland, theright kind of idea flow (particularly through face-to-face interaction) makes teams smarter.

#2. Identifying Specific Skills Gaps With Advanced Analytics

In an era of digital disruption, new types of jobs (like social media director or programmatic advertising manager) are continually cropping up. This means that gauging recruitment success based on number of roles filled is now an exercise in futility.

In 2018, organizations that can identify the specific skills they need — not just the requisitions required to fill — will have a leg up in the war for talent. Big data analytics have evolved to the point where it is now possible to make hiring plans based on specific work activities and attributes of top performers. Those businesses who can recruit based on fit and skill will land the best hires this year.

#3. Using Data to Determine Who Does the Job: Robot or Human?

This year, organizations will continue to grapple with the question of whether to hire more people or implement more automation.

According to a McKinsey & Company report, there are many factors to consider beyond technical feasibility when addressing this question, including the cost of labor and related supply-and-demand dynamics. “If workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it,” states the report. Moreover, with many of the customer-facing decisions (the self-serve kiosk vs. the in-person agent, for example) the cost equation can’t be reduced to simple accounting.

In 2018, data-driven HR leaders, working closely with line managers, will be in the best position to fully understand when human labor is more productive and/or cost-effective than technology.

#4. Predicting Job Changes With New Forms of Artificial Intelligence

This year, more accurate predictions — made possible by a new branch of Artificial Intelligence called “deep learning” — will make it easier to match workforce supply with demand.

Many predictive technologies are based on simple regressions or static models, instead of machine learning (which refers to the process of inferring the unknown based on patterns in historical data). With deep learning, algorithms are based on data generated by several layers of machine learning. This has been proven by data scientists to be up to 17 times more accurate than other methods.

More organizations will use systems that leverage deep learning to make workforce predictions. By forecasting when, how many, and which employees are likely to leave, for example, businesses will be better able to plan for hiring.

#5. Measuring Learning Effectiveness With Applied Big Data

Critical skills gaps and new types of learning programs — from rapid e-learning to mobile e-learning — have fueled the global e-learning market, which is poised to reach approximately $331 billion by 2025.

Yet, measuring how learning and development impacts business results is still a challenge for learning leaders: According to an Association for Talent Development report, only 15% of talent development professionals measure the ROI of any learning programs.

In 2018, more organizations will turn to modern learning analytics technology to analyze the effectiveness of learning programs. With new applied big data solutions — platforms that are pre-built with industry best practices — talent development professionals can connect the necessary HR and business systems together to make it faster and easier to analyze learning data.

Data in 2018 and Beyond: A New Vision for an Immersive Future

The data age is undoubtedly changing the way businesses make decisions about people. But how about the ways in which we interact with this data? Will there be a fundamental shift here too?

Over the course of my career in Business Intelligence, I have encountered many leaders who assumed that Natural Language Processing (NLP) would be the definitive progression of the analytics interface, with non-technical workers verbally asking questions and an automated system offering an appropriate response.

Indeed, while NLP has become a dazzling source of innovation in recent years (think of Alexa, Siri, or Cortana ) what these systems still can’t do is help you to figure out the right question to ask. The value in analytics comes from asking the right questions, and making sure users understand the answers.

New use cases for immersive visualization have generated enthusiasm for visual-first interfaces. As described by big data expert Bernard Marr, Virtualitics (which announced its initial round of funding last April) is a new startup that offers businesses the “intriguing possibility” of stepping inside the data with virtual reality and augmented reality.

Ultimately, in 2018, we will not only witness changes in the analytics technology itself, but also see a shift in how we imagine the analytics interface of the future. Taking the longview, one thing is apparent: when it comes to the data experience, we’ve only seen the tip of the iceberg.

Dave enjoys problems that require both logical and creative solutions, and thus exercise both his left- and right-brain. He started out his career in the 90s writing code as a computer programmer, and then moved on to product management, marketing and general management roles. Dave has a strong background in analytics, having played a key role in the analytics businesses at SAP, Business Objects, and Crystal Decisions. At Visier, he looks after product and market strategy. A proficient do-it-yourselfer (he made his own PVR for fun), Dave’s hobbies include the logical and creative challenges of cooking, home brewing, and photography.